Pricing optimization apparatus and method
A method or apparatus for automatically determining an optimum price for an offer by a first entity to a customer entity, comprising: obtaining a demand function representative of the customer, using the demand function and other factors include an intended product margin, building a goal function representative of goals of said first entity, and automatically optimizing the goal function for the margin, therefrom to generate the offer.
1. A method of automatically determining optimum prices for offers by a first entity to customer entities, comprising the steps of:
obtaining
a set of demand functions individually parameterized for respective customer entities, and
using said set and product margins, constructing a goal function representative of goals of said first entity, and
automatically maximizing said goal function using said set, to optimize said margins, therefrom to generate optimized offers to corresponding customer entities and
wherein said demand function comprises expected behavior in respect of said offer together with expected behavior in respect of future offers, and wherein said automatically optimizing comprises taking into account said expected behaviors in respect of future offers and
wherein said automatically optimizing comprises iteratively optimizing backwardly from a furthest future offer to a present offer, in each iteration considering an offer respective to said iteration and an immediately previous offer.
2. A method of automatically determining optimum prices for offers by a first entity to customer entities, comprising the steps of:
obtaining
a set of demand functions individually parameterized for respective customer entities,
using said set and product margins, constructing a goal function representative of goals of said first entity, and
automatically maximizing said goal function using said set, to optimize said margins, therefrom to generate optimized offers to corresponding customer entities and
wherein said constructing of said goal function comprises incorporating constraints into said goal function and
wherein said incorporating of said constraints comprises building an effective goal function using a Lagrangian multiplier to represent said constraints and
further comprising forming a set of non-liner equations from said Lagrangian multipliers and solving said non-linear equations,
wherein said automatically optimizing comprises iteratively optimizing backwardly from a furthest future offer to a present offer, in each iteration considering an offer respective to said iteration and an immediately previous offer, in each stage using a lagrangian multiplier obtained by solution of said non-linear equations.